On approximations of the beta process in latent feature models: point processes approach

DOI10.1007/s13171-017-0103-9zbMath1387.62063OpenAlexW2619373208MaRDI QIDQ1744721

Mahmoud Zarepour, Luai Al-Labadi

Publication date: 19 April 2018

Published in: Sankhyā. Series A (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s13171-017-0103-9

zbMATH Keywords

simulationbeta processfinite dimensional approximationlatent feature modelsFerguson and Klass representation

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20) Bayesian inference (62F15) Random measures (60G57)

Related Items (3)

On functional central limit theorems of Bayesian nonparametric priors ⋮ A new construction of covariance functions for Gaussian random fields ⋮ On metrizing vague convergence of random measures with applications on Bayesian nonparametric models

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• On a rapid simulation of the Dirichlet process
• On asymptotic properties and almost sure approximation of the normalized inverse-Gaussian process
• Nonparametric Bayes estimators based on beta processes in models for life history data
• A new algorithm to generate beta processes
• Beta-stacy processes and a generalization of the Pólya-Urn scheme
• Nonparametric Bayesian estimators for counting processes
• On simulations from the two-parameter Poisson-Dirichlet process and the normalized inverse-Gaussian process
• Completely random measures
• A Bayesian analysis of some nonparametric problems
• Beta processes, stick-breaking and power laws
• A Bayesian nonparametric goodness of fit test for right censored data based on approximate samples from the beta-Stacy process
• Bayesian analysis of multistate event history data: beta-Dirichlet process prior
• Hidden Markov Models With Stick-Breaking Priors
• Exact and approximate sum representations for the Dirichlet process
• Nonparametric Bayesian Dictionary Learning for Analysis of Noisy and Incomplete Images
• Goodness-of-fit tests based on the distance between the Dirichlet process and its base measure
• A Representation of Independent Increment Processes without Gaussian Components

This page was built for publication: On approximations of the beta process in latent feature models: point processes approach